
  ___  ____  ____  ____  ____ (R)
 /__    /   ____/   /   ____/
___/   /   /___/   /   /___/   15.1   Copyright 1985-2017 StataCorp LLC
  Statistics/Data Analysis            StataCorp
                                      4905 Lakeway Drive
     MP - Parallel Edition            College Station, Texas 77845 USA
                                      800-STATA-PC        http://www.stata.com
                                      979-696-4600        stata@stata.com
                                      979-696-4601 (fax)

32-user 64-core Stata network perpetual license:
       Serial number:  501506200495
         Licensed to:  Harvard-MIT Data Center
                       Cambridge, MA

Notes:
      1.  Stata is running in batch mode.
      2.  Unicode is supported; see help unicode_advice.
      3.  More than 2 billion observations are allowed; see help obs_advice.
      4.  Maximum number of variables is set to 5000; see help set_maxvar.

. do "dofiles/2_estimates_figure3.do" 

. /****************************************************************************
> *****************************
> Replication Files for Housing Discrimination and the Toxics Exposure Gap in t
> he United States: 
> Evidence from the Rental Market  by Peter Christensen, Ignacio Sarmiento-Barb
> ieri and Christopher Timmins
> *****************************************************************************
> ****************************/
. 
. clear all

. set matsize 11000

. 
. use "../stores/toxic_discrimination_data.dta"

. 
. 
. keep if sample==1
(801 observations deleted)

. 
. loc quartiles 4

. 
. 
. set seed 1010101

. 
. *****************************************************************************
> *******************
. * Minority
. *****************************************************************************
> *******************
. 
. 
. 
. disc_boot choice Minority_dec2 Minority_dec3 Minority_dec4 , varlist(i.gender
>  i.education_level i.order)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -925.74055  
Iteration 1:   log pseudolikelihood = -916.48806  
Iteration 2:   log pseudolikelihood = -916.46513  
Iteration 3:   log pseudolikelihood = -916.46513  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(8)      =     120.56
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -916.46513               Pseudo R2         =     0.0833

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5211012   .1376313    -3.79   0.000    -.7474846   -.2947179
Minority_d~3 |  -.3482699   .1358951    -2.56   0.010    -.5717975   -.1247423
Minority_d~4 |   .1434486   .1312026     1.09   0.274    -.0723605    .3592577
             |
      gender |
       male  |  -.3129561   .0870571    -3.59   0.000    -.4561522     -.16976
             |
education_~l |
        low  |  -.1674876    .115987    -1.44   0.149    -.3582693     .023294
     medium  |  -.2999306   .1376423    -2.18   0.029    -.5263321   -.0735291
             |
       order |
          2  |  -.3356589   .2439099    -1.38   0.169     -.736855    .0655371
          3  |  -.9002829   .2019871    -4.46   0.000    -1.232522   -.5680438
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5211012   .1554043    -3.35   0.005    -.7963119   -.2458906
Minority_d~3 |  -.3482699   .1367799    -2.55   0.024     -.590498   -.1060418
Minority_d~4 |   .1434486   .1396337     1.03   0.323    -.1038334    .3907307
------------------------------------------------------------------------------

. 
. 
. 
. mat def b=e(b)

. mat def V=e(V)

. 
. mat def C=J(3,3,.)

. forvalues i= 1/3{
  2.         mat C[`i',1]=b[1,`i'] // coefs
  3.         mat C[`i',2]=V[`i',`i'] // var
  4.         mat C[`i',3]=e(df_`i') // dofs
  5.         }

. 
. 
. 
. loc quartiles 4

. matrix define M=J(`quartiles'-1,5,.)

. forvalues j = 2/`quartiles'{
  2.         loc i=  `j'-1
  3.                 
.         matrix M[`i',1] = C[`i',1] - invttail(C[`i',3],0.05)*sqrt(C[`i',2])
  4.         matrix M[`i',2] = C[`i',1] 
  5.         matrix M[`i',3] = C[`i',1] + invttail(C[`i',3],0.05)*sqrt(C[`i',2]
> )
  6. 
.         sum Minority if dec`j'==1
  7.         matrix M[`i',4]=`r(N)'
  8.         sum choice if White==1 &  dec`j'==1
  9.         matrix M[`i',5]=`r(mean)'
 10.         
.         mat list M
 11. 
. }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      1,800    .6666667    .4715355          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        600    .3916667    .4885301          0          1

M[3,5]
            c1          c2          c3          c4          c5
r1  -.79631192  -.52110124  -.24589056        1800   .39166667
r2           .           .           .           .           .
r3           .           .           .           .           .

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      3,342    .6666667    .4714751          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      1,114    .4156194    .4930498          0          1

M[3,5]
            c1          c2          c3          c4          c5
r1  -.79631192  -.52110124  -.24589056        1800   .39166667
r2  -.59049802  -.34826993  -.10604185        3342   .41561939
r3           .           .           .           .           .

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      1,581    .6666667    .4715537          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        527    .3529412    .4783387          0          1

M[3,5]
            c1          c2          c3          c4          c5
r1  -.79631192  -.52110124  -.24589056        1800   .39166667
r2  -.59049802  -.34826993  -.10604185        3342   .41561939
r3  -.10383338   .14344864   .39073066        1581   .35294118

. 
. 
. 
. *--------------------------------------------------------------------
. preserve

. clear

. svmat M
number of observations will be reset to 3
Press any key to continue, or Break to abort
number of observations (_N) was 0, now 3

. gen n=_n

. replace M1=exp(M1)
(3 real changes made)

. replace M2=exp(M2)
(3 real changes made)

. replace M3=exp(M3)
(3 real changes made)

. 
. 
. rename M1 lci

. rename M2 coef

. rename M3 uci

. rename M4 obs

. rename M5 c_mean

. rename n deciles

. 
. 
. 
. save "../stores/aux/interquartile_toxconc_minority_bootcl.dta", replace
(note: file ../stores/aux/interquartile_toxconc_minority_bootcl.dta not found)
file ../stores/aux/interquartile_toxconc_minority_bootcl.dta saved

. restore

. 
. 
. *****************************************************************************
> *******************
. * African American vs Hispanic/LatinX
. *****************************************************************************
> *******************
. disc_boot choice  Hispanic_dec2  Hispanic_dec3 Hispanic_dec4 ///
>                                         Black_dec2  Black_dec3 Black_dec4 ///
>                                          , varlist(i.gender i.education_level
>  i.order)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -910.23067  
Iteration 1:   log pseudolikelihood = -899.77346  
Iteration 2:   log pseudolikelihood = -899.71428  
Iteration 3:   log pseudolikelihood = -899.71427  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(11)     =    1743.92
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -899.71427               Pseudo R2         =     0.1000

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Hispanic_d~2 |  -.2521266    .134985    -1.87   0.062    -.4741571    -.030096
Hispanic_d~3 |  -.0790205   .0917728    -0.86   0.389    -.2299734    .0719324
Hispanic_d~4 |   .2938816   .1543903     1.90   0.057     .0399321    .5478311
  Black_dec2 |  -.8083192   .1492354    -5.42   0.000    -1.053789   -.5628489
  Black_dec3 |  -.6198705   .2013075    -3.08   0.002    -.9509919   -.2887491
  Black_dec4 |  -.0088093   .1651946    -0.05   0.957    -.2805303    .2629117
             |
      gender |
       male  |  -.3068188   .0902902    -3.40   0.001     -.455333   -.1583047
             |
education_~l |
        low  |  -.2085472   .1269706    -1.64   0.100    -.4173952    .0003009
     medium  |  -.3217568   .1289106    -2.50   0.013    -.5337959   -.1097176
             |
       order |
          2  |  -.3274132   .2524973    -1.30   0.195    -.7427343    .0879078
          3  |  -.9038518   .2104678    -4.29   0.000    -1.250041    -.557663
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Hispanic_d~2 |  -.2521266   .1399496    -1.80   0.095    -.4999679   -.0042852
Hispanic_d~3 |  -.0790205   .0933243    -0.85   0.412    -.2442917    .0862507
Hispanic_d~4 |   .2938816   .1755685     1.67   0.118    -.0170384    .6048017
  Black_dec2 |  -.8083192   .1853143    -4.36   0.001    -1.136498   -.4801399
  Black_dec3 |  -.6198705   .1960878    -3.16   0.008     -.967129    -.272612
  Black_dec4 |  -.0088093   .1656711    -0.05   0.958    -.3022017    .2845831
------------------------------------------------------------------------------

. 
. 
. mat def b=e(b)

. mat def V=e(V)

. 
. *Matrices for boottest estimates
. *Hispanics
. mat def H_C=J(3,3,.)

. forvalues i= 1/3{
  2.         mat H_C[`i',1]=b[1,`i']
  3.         mat H_C[`i',2]=V[`i',`i']
  4.         mat H_C[`i',3]=e(df_`i')
  5.         }

. 
. *Af. Am.        
. mat def H_B=J(3,3,.)

. forvalues i= 4/6{
  2.         loc j=`i'-3
  3.         mat H_B[`j',1]=b[1,`i']
  4.         mat H_B[`j',2]=V[`i',`i']
  5.         mat H_B[`j',3]=e(df_`i')
  6.         }

.         
. *Matrices with results to export and plot
. matrix define H=J(`quartiles'-1,5,.)

. 
. forvalues j = 2/`quartiles'{
  2.         loc i=  `j'-1
  3.         
.         matrix H[`i',1] = H_C[`i',1] - invttail(H_C[`i',3],0.05)*sqrt(H_C[`i'
> ,2])
  4.         matrix H[`i',2] = H_C[`i',1] 
  5.         matrix H[`i',3] = H_C[`i',1] + invttail(H_C[`i',3],0.05)*sqrt(H_C[
> `i',2])
  6.         
.         sum Hispanic if dec`j'==1
  7.         matrix H[`i',4]=`r(N)'
  8.         sum choice if White==1 &  dec`j'==1
  9.         matrix H[`i',5]=`r(mean)'
 10.         
. }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Hispanic |      1,800    .3333333    .4715355          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        600    .3916667    .4885301          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Hispanic |      3,342    .3333333    .4714751          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      1,114    .4156194    .4930498          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Hispanic |      1,581    .3333333    .4715537          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        527    .3529412    .4783387          0          1

. 
. 
. 
. matrix define B=J(`quartiles'-1,5,.)

. 
. forvalues j = 2/`quartiles'{
  2.         loc i=  `j'-1
  3.         
.         matrix B[`i',1] = H_B[`i',1] - invttail(H_B[`i',3],0.05)*sqrt(H_B[`i'
> ,2])
  4.         matrix B[`i',2] = H_B[`i',1] 
  5.         matrix B[`i',3] = H_B[`i',1] + invttail(H_B[`i',3],0.05)*sqrt(H_B[
> `i',2])
  6.         
.         sum Black if dec`j'==1
  7.         matrix B[`i',4]=`r(N)'
  8.         sum choice if White==1 &  dec`j'==1
  9.         matrix B[`i',5]=`r(mean)'
 10.         
. }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       Black |      1,800    .3333333    .4715355          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        600    .3916667    .4885301          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       Black |      3,342    .3333333    .4714751          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      1,114    .4156194    .4930498          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       Black |      1,581    .3333333    .4715537          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        527    .3529412    .4783387          0          1

. 
. mat list B

B[3,5]
            c1          c2          c3          c4          c5
r1  -1.1364985  -.80831917  -.48013989        1800   .39166667
r2  -.96712898  -.61987051  -.27261204        3342   .41561939
r3   -.3022017  -.00880928   .28458313        1581   .35294118

. 
. clogit choice  Hispanic_dec2 Hispanic_dec3 Hispanic_dec4 ///
>                                          Black_dec2 Black_dec3 Black_dec4 ///
>                                          i.gender i.education_level i.order ,
>  or group(Address)  cl(Zip_Code) level(90)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -910.23067  
Iteration 1:   log pseudolikelihood = -899.77346  
Iteration 2:   log pseudolikelihood = -899.71428  
Iteration 3:   log pseudolikelihood = -899.71427  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(11)     =    1743.92
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -899.71427               Pseudo R2         =     0.1000

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice | Odds Ratio   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Hispanic_d~2 |   .7771464   .1049031    -1.87   0.062     .6224095    .9703523
Hispanic_d~3 |    .924021      .0848    -0.86   0.389     .7945547    1.074583
Hispanic_d~4 |   1.341625    .207134     1.90   0.057      1.04074    1.729498
  Black_dec2 |   .4456064   .0665002    -5.42   0.000     .3486142    .5695841
  Black_dec3 |   .5380141   .1083063    -3.08   0.002     .3863576    .7492002
  Black_dec4 |   .9912294   .1637458    -0.05   0.957     .7553831    1.300712
             |
      gender |
       male  |   .7357839   .0664341    -3.40   0.001     .6342367    .8535897
             |
education_~l |
        low  |   .8117627     .10307    -1.64   0.100     .6587605    1.000301
     medium  |   .7248745    .093444    -2.50   0.013     .5863749    .8960871
             |
       order |
          2  |   .7207858   .1819965    -1.30   0.195     .4758111    1.091887
          3  |   .4050067   .0852409    -4.29   0.000     .2864932    .5725455
------------------------------------------------------------------------------

. 
. di (`e(N)'+`e(N_drop)')/3
2241

. *--------------------------------------------------------------------
. preserve

. clear

. svmat B
number of observations will be reset to 3
Press any key to continue, or Break to abort
number of observations (_N) was 0, now 3

. gen n=_n

. replace B1=exp(B1)
(3 real changes made)

. replace B2=exp(B2)
(3 real changes made)

. replace B3=exp(B3)
(3 real changes made)

. 
. 
. 
. rename B1 lci

. rename B2 or

. rename B3 uci

. rename B4 obs

. rename B5 c_mean

. rename n deciles

. 
. save "../stores/aux/interquartile_toxconc_AA_bootcl.dta", replace
(note: file ../stores/aux/interquartile_toxconc_AA_bootcl.dta not found)
file ../stores/aux/interquartile_toxconc_AA_bootcl.dta saved

. restore

. 
. 
. 
. 
. *--------------------------------------------------------------------
. preserve

. clear

. svmat H
number of observations will be reset to 3
Press any key to continue, or Break to abort
number of observations (_N) was 0, now 3

. gen n=_n

. replace H1=exp(H1)
(3 real changes made)

. replace H2=exp(H2)
(3 real changes made)

. replace H3=exp(H3)
(3 real changes made)

. 
. 
. 
. rename H1 lci

. rename H2 or

. rename H3 uci

. rename H4 obs

. rename H5 c_mean

. rename n deciles

. 
. 
. 
. save "../stores/aux/interquartile_toxconc_Hisp_bootcl.dta", replace
(note: file ../stores/aux/interquartile_toxconc_Hisp_bootcl.dta not found)
file ../stores/aux/interquartile_toxconc_Hisp_bootcl.dta saved

. restore

. 
. *****************************************************************************
> *******************
. * Male vs Female
. *****************************************************************************
> *******************
. 
. 
. 
. gen male=(gender==2)

. gen female=(gender==1)

. 
. 
. 
. forvalues i = 2/`quartiles'{
  2.         foreach genero in male female {
  3.                 gen Minority_dec`i'_`genero'=Minority*dec`i'*`genero'
  4.         }
  5. }

. 
. 
. loc quartiles 4

. 
. 
. disc_boot choice  Minority_dec2_female Minority_dec3_female Minority_dec4_fem
> ale ///
>                           Minority_dec2_male   Minority_dec3_male Minority_de
> c4_male ///
>                                          , varlist(i.gender i.education_level
>  i.order)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -923.89616  
Iteration 1:   log pseudolikelihood = -913.83402  
Iteration 2:   log pseudolikelihood = -913.80594  
Iteration 3:   log pseudolikelihood = -913.80594  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(11)     =     178.22
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -913.80594               Pseudo R2         =     0.0860

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Min~2_female |  -.2607176   .1229937    -2.12   0.034    -.4630242    -.058411
Min~3_female |  -.1562993   .1063811    -1.47   0.142    -.3312806     .018682
Min~4_female |   .2930419    .108046     2.71   0.007     .1153221    .4707617
Minor~2_male |  -.7504773   .2423545    -3.10   0.002    -1.149115   -.3518396
Minor~3_male |  -.5745522   .2096812    -2.74   0.006    -.9194471   -.2296573
Minor~4_male |   -.009659   .2212175    -0.04   0.965    -.3735294    .3542115
             |
      gender |
       male  |  -.0396958   .1657158    -0.24   0.811     -.312274    .2328823
             |
education_~l |
        low  |  -.1687538   .1188568    -1.42   0.156    -.3642559    .0267483
     medium  |  -.3072229   .1411242    -2.18   0.029    -.5393516   -.0750942
             |
       order |
          2  |  -.3375224   .2445573    -1.38   0.168    -.7397834    .0647386
          3  |  -.9114601   .2038576    -4.47   0.000    -1.246776   -.5761442
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Min~2_female |  -.2607176   .1258362    -2.07   0.059    -.4835651   -.0378702
Min~3_female |  -.1562993   .1088817    -1.44   0.175    -.3491216     .036523
Min~4_female |   .2930419   .1157717     2.53   0.025      .088018    .4980659
Minor~2_male |  -.7504773   .2718969    -2.76   0.016    -1.231989    -.268966
Minor~3_male |  -.5745522   .2087617    -2.75   0.016    -.9442553    -.204849
Minor~4_male |   -.009659   .2213515    -0.04   0.966    -.4016578    .3823399
------------------------------------------------------------------------------

. 
. 
. mat def b=e(b)

. mat def V=e(V)

. 
. 
. 
. *Matrices for boottest estimates
. *Females
. mat def M_F=J(3,3,.)

. forvalues i= 1/3{
  2.         mat M_F[`i',1]=b[1,`i']
  3.         mat M_F[`i',2]=V[`i',`i']
  4.         mat M_F[`i',3]=e(df_`i')
  5.         }

. 
. 
. *Males  
. mat def M_M=J(3,3,.)

. forvalues i= 4/6{
  2.         loc j=`i'-3
  3.         mat M_M[`j',1]=b[1,`i']
  4.         mat M_M[`j',2]=V[`i',`i']
  5.         mat M_M[`j',3]=e(df_`i')
  6.         }

. 
. 
. *Matrices with results to export and plot
. 
. matrix define B=J(`quartiles'-1,5,.)

. 
. forvalues j = 2/`quartiles'{
  2.         loc i=  `j'-1
  3.         matrix B[`i',1] = M_F[`i',1] - invttail(M_F[`i',3],0.05)*sqrt(M_F[
> `i',2])
  4.         matrix B[`i',2] = M_F[`i',1] 
  5.         matrix B[`i',3] = M_F[`i',1] + invttail(M_F[`i',3],0.05)*sqrt(M_F[
> `i',2])
  6. 
.         sum Minority if dec`j'==1
  7.         matrix B[`i',4]=`r(N)'
  8.         sum choice if White==1 &  dec`j'==1
  9.         matrix B[`i',5]=`r(mean)'
 10.         
.         
. }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      1,800    .6666667    .4715355          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        600    .3916667    .4885301          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      3,342    .6666667    .4714751          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      1,114    .4156194    .4930498          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      1,581    .6666667    .4715537          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        527    .3529412    .4783387          0          1

. mat list B

B[3,5]
            c1          c2          c3          c4          c5
r1  -.48356506  -.26071762  -.03787018        1800   .39166667
r2  -.34912158  -.15629927   .03652305        3342   .41561939
r3   .08801799   .29304193   .49806586        1581   .35294118

. 
. *--------------------------------------------------------------------
. preserve

. clear

. svmat B
number of observations will be reset to 3
Press any key to continue, or Break to abort
number of observations (_N) was 0, now 3

. gen n=_n

. replace B1=exp(B1)
(3 real changes made)

. replace B2=exp(B2)
(3 real changes made)

. replace B3=exp(B3)
(3 real changes made)

. 
. 
. rename B1 lci

. rename B2 or

. rename B3 uci

. 
. 
. rename n deciles

. 
. 
. save "../stores/aux/interquartile_toxconc_minority_female_bootcl.dta", replac
> e
(note: file ../stores/aux/interquartile_toxconc_minority_female_bootcl.dta not 
> found)
file ../stores/aux/interquartile_toxconc_minority_female_bootcl.dta saved

. restore

. *--------------------------------------------------------------------
. 
. 
. 
. 
. matrix define B=J(`quartiles'-1,5,.)

. 
. forvalues j = 2/`quartiles'{
  2.         loc i=  `j'-1
  3.         matrix B[`i',1] = M_M[`i',1] - invttail(M_M[`i',3],0.05)*sqrt(M_M[
> `i',2])
  4.         matrix B[`i',2] = M_M[`i',1] 
  5.         matrix B[`i',3] = M_M[`i',1] + invttail(M_M[`i',3],0.05)*sqrt(M_M[
> `i',2])
  6. 
.         
.         *sum Minority_dec`j'_male
.         *matrix B[`i',4]=`r(sum)'
.         sum Minority if dec`j'==1
  7.         matrix B[`i',4]=`r(N)'
  8.         
.         sum choice if White==1 &  dec`j'==1
  9.         matrix B[`i',5]=`r(mean)'
 10.         mat list B
 11.         
. }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      1,800    .6666667    .4715355          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        600    .3916667    .4885301          0          1

B[3,5]
            c1          c2          c3          c4          c5
r1  -1.2319887  -.75047733  -.26896601        1800   .39166667
r2           .           .           .           .           .
r3           .           .           .           .           .

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      3,342    .6666667    .4714751          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      1,114    .4156194    .4930498          0          1

B[3,5]
            c1          c2          c3          c4          c5
r1  -1.2319887  -.75047733  -.26896601        1800   .39166667
r2  -.94425531  -.57455217  -.20484904        3342   .41561939
r3           .           .           .           .           .

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    Minority |      1,581    .6666667    .4715537          0          1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |        527    .3529412    .4783387          0          1

B[3,5]
            c1          c2          c3          c4          c5
r1  -1.2319887  -.75047733  -.26896601        1800   .39166667
r2  -.94425531  -.57455217  -.20484904        3342   .41561939
r3  -.40165778  -.00965895   .38233987        1581   .35294118

. 
. 
. *--------------------------------------------------------------------
. preserve

. clear

. svmat B
number of observations will be reset to 3
Press any key to continue, or Break to abort
number of observations (_N) was 0, now 3

. gen n=_n

. replace B1=exp(B1)
(3 real changes made)

. replace B2=exp(B2)
(3 real changes made)

. replace B3=exp(B3)
(3 real changes made)

. 
. 
. rename B1 lci

. rename B2 or

. rename B3 uci

. 
. rename n deciles

. 
. 
. save "../stores/aux/interquartile_toxconc_minority_male_bootcl.dta", replace
(note: file ../stores/aux/interquartile_toxconc_minority_male_bootcl.dta not fo
> und)
file ../stores/aux/interquartile_toxconc_minority_male_bootcl.dta saved

. restore

. *--------------------------------------------------------------------
. 
. *end
. 
end of do-file
